M. Corbeels 1,2
1 Agro-ecology and Sustainable Intensification of Annual Crops, CIRQD, Avenue Agropolis, 34398 Montpel-lier, Cedex 5, France
2 Sustainable Intensification Program, CIMMYT, United Nations Avenue, Nairobi, Kenya Introduction
Carbon (C) and nitrogen (N) dynamics in the soil can be simulated by a number of ap-proaches. Simple two-compartment models comprising a labile and stable organic matter pool can be analytically solved and parameter estimation for a given situation is relatively simple (e.g. ICBM, Kätterer and Andrén, 2001). However, these types of models do not incorporate important feedbacks of soil C and N to changing environ-ment. More comprehensive models, such as CENTURY (Parton et al., 1987), have been developed for this purpose. Yet, most of these models do not consider explicitly mi-crobial physiology as the driving factor of N immobilization-mineralization turnover, while this is fundamental for an adequate description of decomposition of soil organic matter (SOM) and soil N supply to crops.
Materials and Methods
The SoilC&N model includes above- and below-ground plant residue pools and three SOM pools (microbial biomass, Young and Old SOM) with different turnover times (Fig. 1). The distinctive features of this model are: 1) growth of microbial biomass is the process that drives N immobilization-mineralization, and microbial succession is simu-lated; 2) decomposition of plant residues may be N-limited, depending on soil inorgan-ic N availability relative to N requirements for minorgan-icrobial growth; 3) N:C ratio of minorgan-icro- micro-bial biomass active in decomposing plant residues is a function of residue quality and soil inorganic N availability; 4) 'quality' of plant residues is expressed in terms of meas-urable biochemical fractions; and 5) C:N ratios of SOM pools are not prescribed but are instead simulated model output variables. Nitrogen is mineralized to, or immobilized from, the soil inorganic N pool to maintain the C:N ratio of decomposing microbial biomass within a specified range. Balancing potential microbial N demand against inorganic N availability determines whether the activity of decomposers is limited by N. If so, then simulated microbial use efficiency and decomposition fluxes are reduced.
Results and Discussion
SoilC&N can be used as a stand-alone model or coupled to a crop growth model to simulate within-season soil N supply from SOM and added organic sources to crops.
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Figure 1. Pools and fluxes of (a) C and (b) N in the SoilC&N model. MP: metabolic pool; HCP: holocellulosic pool; LCP: ligno-cellulosic pool; L: lignin; SOM: soil organic matter; Sm: stabilisation coefficient for microbial
biomass; Sy: stabilisation coefficient for Young SOM (from Corbeels et al., 2005).
The model responds to quality of added organic matter and predicts N immobilization or mineralization rates in time. The N immobilization peak depends on the biochemical quality of the plant residues and the available inorganic N. When soil inorganic N be-comes severely limiting, decomposition of residues is slowed down. With a proper parameterization of plant residue ‘quality’, the model can acceptably predict N dynam-ics from crop residues ranging from green leguminous leaves to woody residues. Cou-pled to a crop growth model, SoilC&N is particularly suited for simulating the impacts of management or land-use changes on soil C storage and long-term N availability for plants. For example, the model is able to predict long-term storage of soil C following a change in land-use from forest to cropland, as a result of simulated changes in micro-bial activity, soil N availability and SOM C:N ratios to changes in plant residue quantity and quality. The incorporation of the feedbacks in the model between plant residue quality, N availability and microbial activity increases the mechanistic integrity of the model, compared to other models such as CENTURY or RothC (Coleman et al., 1997).
Conclusions
The ability of SoilC&N to adequately describe both short-term events such as soil N supply during one growing season, and long-term dynamics, e.g. soil C storage over several decades, is an important asset when coupling to a crop growth model.
References
Coleman, K., D. Jenkinson, G. Crocker et al., (1997). Geoderma, 81: 29-44.
Corbeels, M., R.E. McMurtrie, D.A. Pepper et al., (2005). Ecol. Model., 187: 426-448.
Kätterer, T. and O. Andrén (2001). Ecol. Model., 136: 191-207.
Parton, W.J., D.S. Schimel, C.V. Cole et al., (1987) Soil. Sci. Soc. Am. J., 51: 1173-1179.
Microbial
Foliar and fine root litter Woody litter
MP HCP WP
L LCP L
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Modelling the genetic variability and genotype by environment inter-actions for leaf growth and senescence in wheat
A. Dambreville 1,2 – A. Maiorano 1,2 – P. Martre 1,2
1 INRA, UMR759 Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux, F-34 060 Mont-pellier, France, e-mail: pierre.martre@supagro.inra.fr
2 Montpellier SupAgro, UMR759 2 Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux, F-34 060 Montpellier, France
Introduction
The ability to predict leaf area index dynamics is crucial to predict crop growth and yield, particularly under conditions of limited resource supply (Ewert, 2004). Leaf area dynamic depends on several factors such as meteorological conditions, crop management or genetics. Most wheat crop models simulate leaf area index using a
“big-leaf” approach where the whole canopy is treated as one big-leaf and several models simulate leaf area index indirectly from biomass production (Parent and Tardieu, 2014). However, because the response of leaf expansion and senescence to environmental factors strongly depends on leaf age and position (plastochron index), modelling the expansion and senescence of individual leaf is critical to predict the effect of combined stresses. Modelling the ontogeny and expansion of individual leaves also allows modelling the dynamic of tillers. Functional–structural models describe leaf area and tiller dynamics (e.g. Evers et al., 2005) but they are mainly descriptive and are difficult to parametrize for new genotypes. Here, we describe a new model of leaf area dynamics implemented in SiriusQuality2 wheat model. This model links phenological development with leaf expansion and simulates the coordination between leaf sheath and lamina expansion and between phytomers and tillers. The model was evaluated using detailed field experiments with contrasted water and N supply. Finally, we demonstrated that the model is able to simulate the genetic variability and genotype by environment (GxE) interactions for leaf growth and senescence, and we discussed the use of phenotyping platforms to measure the genotypic parameters of the model on large genetic panels for genetic analysis.
Materials and Methods
SiriusQuality2 is a process-based wheat model composed of seven components modelling the development of the plant and the fluxes of water, N and carbon in the soil-plant-atmosphere continuum (http://www1.clermont.inra.fr/siriusquality/). Leaf expansion is modeled using 14 parameters related to internode, sheath and lamina growth. Daily leaf expansion and senescence is simulated in response to water and N deficit using a supply-demand approach. The most influential parameters were identified thanks to a global sensitivity analysis of the model (Martre et al., 2015) and the genetic variability of three influential parameters of the leaf area dynamics model was determined for a panel of 16 winter wheat modern cultivars grown in the field in France and UK with a range of water and N supply. Three additional parameters
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related to the response of leaf expansion and senescence to water and N deficit were calibrated numerically for the same genetic panel.
Results and Discussion
The number (NLL) and potential size (AreaPL) of the leaves produced after floral initiation, and the potential ratio of the flag leaf to penultimate leaf size (RatioFLPL) strongly influenced leaf area dynamics. These three parameters were measured for 16 modern cultivars in field experiments with unlimited water and N supply. The range for these parameters were 3.9-5.6 leaves, 20.1-36.7 cm² and 0.67-1.27, respectively.
Across the cultivars, the RatioFLPL was negatively correlated to the AreaPL meaning that a larger potential leaf size was related to a smaller flag leaf compared to the penultimate leaf. Variance analyses showed that the variability of these parameters were mainly due to genotypic effects. Three parameters related to the critical N mass per unit of leaf surface area of growing leaves and to the response of leaf expansion and senescence to water deficit were calibrated for the same genetic panel under conditions of limited resource supply. Across all environments and genotypes, the root mean squared relative error for LAI averaged 25 %. The model was able to capture around 60 % and 98 % of the genotypic (ranging from 2.5 to 3.6 m2 m-2) and environmental (1.3 to 4.9 m2 m-2) variability of LAI at anthesis.
Conclusions
We conclude that the leaf area model presented here is able to explain a large part of the genotypic and environmental effects on wheat leaf area dynamics using a minimum set of genotype-specific parameters. The three parameters related to the developmental pattern of potential laminae surface area are mainly under genetic control and can be easily determined in the field or in control conditions on large genetic panels. The three parameters related to the response of leaf expansion and senescence to N and water supply were calibrated numerically. This calibration requires large datasets with a range of water and N supply. However, these parameters could also be determined under control conditions in plant phenotyping platforms.
Acknowledgements
This work was supported by the European Union’s Seventh Framework Programme (FP7/2007–2013; grant no. FP7-613556). AM has received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement n° PCOFUND-GA-2010-267196.
References
Evers, J.B., J. Vos, C. Fournier et al., (2005). New Phytologist, 166: 801–812.
Ewert, F. (2004). Annals of Botany, 93: 619–627.
Martre, P., J. He, J. Le Gouis, M.A. Semenov (2015). Journal of Experimental Botany, 66: 3581–3598.
Parent, B., and F. Tardieu (2014). Journal of Experimental Botany, 65: 6179–6189.
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1 INRA, Lusignan, France; jean-louis.durand@lusignan.inra.fr
2 University of Florida, Gainesville, United States of America
3 Technical University of Madrid, UPM, Madrid, Spain
4 Johann Heindrich von Thunen Institute, Braunschweig, Germany
5 NASA Goddard Institute for Space Studies, New York city, USA
6 ASRU, USDA-ARS, Fort Collins, Colorado, USA.
7 CPSRU, USDA-ARS, Stoneville, Mississippi, USA.
8 Department of Geological Sciences, Michigan State University, Michigan, USA
9 CIRAD, UMR TETIS, Montpellier, France
10INRA, Avignon, France
11Institute of Biochemical Plant Pathology, Helmholtz Zentrum, München, Neuherberg, Germany
12Tyndall Centre for Climate Change research and School of Environmental Sciences, University of East Anglia, Norwich, UK
13Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
14Institute of Soil Science and Land Evaluation, University of Hohenheim, D-70599 Stuttgart, Germany.
15Institute of Landscape Systems Analysis, ZALF, Leibniz-Centre for Agricultural Landscape Research, Muencheberg, Germany
16School of Environmental and Forest Sciences, University of Washington, Seattle, USA
17Potsdam Institute for Climate Impact Research, Potsdam, Germany
18School of Earth and Environment, University of Leeds, Leeds, UK CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Cali, Colombia International Center for Tropical Agriculture (CIAT), Cali, Colombia
19Natural Resources Institute, Luke, Finland
20Technische Universität Dresden, Germany
21Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
22Crop Systems and Global Change Laboratory, USDA/ARS, Beltsville, USA
23Department of Soil, Water, & Climate, University of Minnesota, Minnesota, USA
24CSIRO, Land and Water, Black Mountain, Australia
25China Agricultural University, Beijing, China Introduction
Maize is a major crop in the world. The ability of crop models to predict the complexity of the interactions behind the yield response to climate and especially to air CO2 con-centration [CO2] needs to be tested (Bassu et al., 2012). Furthermore, the water use is a key issue for assessing our ability to sustain maize yields under future climate, since hotter and dryer conditions may become more frequent. In the study reported here, a Free Air CO2 Enrichment (FACE) showing a very large impact of [CO2] on yield